Comparisons between the various types of neural networks with the data of wide range operational conditions of the Borssele NPP

被引:19
作者
Ayaz, E [1 ]
Seker, S
Barutçu, B
Türkcan, E
机构
[1] Istanbul Tech Univ, Fac Elect & Elect, TR-80626 Istanbul, Turkey
[2] Istanbul Tech Univ, Inst Nucl Energy, TR-80626 Istanbul, Turkey
关键词
Recurrent neural networks; Borssele nuclear power plant (PWR); NPP diagnostic system; anomaly detection by neural networks;
D O I
10.1016/S0149-1970(03)00047-7
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
This paper addresses a trend monitoring in operating nuclear power plant by use of two types of Recurrent Neural Networks (RNN). The interesting feature of the RNN is intrinsic dynamic memory that reflects the current output as well as the previous inputs and outputs are gradually quenched. The first one Elman type of RNN which has a feed-back from hidden layer to the input layer neurons while in the Jordan type, from the outputs of the neural net to the inputs of the neural net. In this paper the theoretical assessment of the both RNNs is given. Both topological structures including Back Propagation (BP) neural network were implemented to the Borssele NPP. Learning achieved from 30% to 100% nominal power at the starting period of the new core 30 September 2001. After learning period the reactor operation is followed by the neural network. Paper will present the reactor system, the real time data collection and the merits of the three types of the neural network applied while in the learning and continuous processing of the changing of the operational conditions. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:381 / 387
页数:7
相关论文
共 12 条
[1]  
AYAZ E, 2001, ANN M NUCL TECHN 200, P110
[2]  
BARUTCU B, 2002, SMORN 8 MAY 26 31 GO
[3]   FINDING STRUCTURE IN TIME [J].
ELMAN, JL .
COGNITIVE SCIENCE, 1990, 14 (02) :179-211
[4]  
ERYUREK E, 1992, NUCL EUROPE WORLDSCA, V1, P72
[5]  
GRUHL HH, 1997, NUCL ENG INT
[6]  
Jordan MI, 1986, Cogn Sci, P531
[7]  
Nabeshima K., 2000, International Journal of Knowledge-Based Intelligent Engineering Systems, V4, P208
[8]   Real-time nuclear power plant monitoring with neural network [J].
Nabeshima, K ;
Suzudo, T ;
Suzuki, K ;
Turkcan, E .
JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY, 1998, 35 (02) :93-100
[9]  
TURKCAN E, 1998, MARCON 98 TENN US
[10]  
TURKCAN E, 1995, NUCL EUROPE WORLDSCA, V11, P31